Dimensionality Reduction Research Articles

Efficient Frontier and Applications in Product Offering and Pricing

Problem definition: The joint assortment and pricing problem is notoriously difficult to solve, especially for large-scale issues subject to various operational constraints arising from business practices. In this paper, we delve into the joint optimization challenge under constrained logit choice models, accounting for product-differentiated price sensitivities across static, randomized, and dynamic contexts. Methodology/results: We develop a unified optimization methodology that applies efficient frontier and dimensional reduction and transforms the joint optimization problem into a single-variable one with respect to the total choice probability. The efficient frontier is employed to transform the nonconcave objective function equivalently into its concave counterpart. We show that the optimal prices are uniquely determined by the common target adjusted markup. The complexity of the mixed combinatorial optimization problem can be reduced by searching over efficient sets of polynomial size. In the randomized assortment and pricing problem in which the product offer sets and prices follow certain distribution, we show that randomization has the potential to elevate the total revenue to the exact efficient frontier, a feat that the static problem may fail to achieve (with a given total choice probability). For dynamic joint product selection and pricing, we find that the optimal policy adopts a simple time-threshold structure that can be precomputed. Furthermore, we illustrate the robustness of our methodology by extending the analysis to a variety of general settings, including the nested logit model. Managerial implications: The proposed methodology contributes to the increasingly popular topic in retail management by significantly reducing the computational complexity of joint product offering and pricing under different constraints. Our results regarding the effects of product set constraints and price bounds provide valuable insights and guidance to practitioners on product offering and pricing in various business scenarios. Funding: C. Ke acknowledges financial support from the National Natural Science Foundation of China [Grant 72101113]. L. Lu acknowledges financial support from the Hong Kong Research Grants Council [Grants 26501021 and 16502423]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0164 .

Dimension Reduction of MUSIC Database for Tailored Clustering Using Lasso

The tailored clustering enabled regionalization (TCER) framework has shown that the prediction error of design parameters at a target site can be reduced by constructing a transformation model from a cluster that only contains sites with geotechnical properties similar to the target site. These sites are retrieved from a global/regional database. However, the similarity between any two sites will reduce when the number of properties increases. TCER will be ineffective if too many properties are included in the database (e.g., structural health monitoring and remote sensing databases). The current study presents a least absolute shrinkage and selection operator for databases with incomplete records (denoted by HMLasso; a dimension reduction method) to improve the performance of TCER in high dimensional databases. HMLasso is utilized to construct a dimensionally reduced database, which includes only the properties relevant to the design parameters, from a high-dimensional database. TCER is then adopted to retrieve a cluster from the dimensionally reduced database for inferring the design parameters at a target site. The capability of HMLasso to enhance the performance of TCER in terms of identifying clusters similar to the target site and reducing the prediction error of design parameters are demonstrated using real-world geotechnical databases.

Regulation of Fibroblast Phenotype in Osteoarthritis Using CDKN1A-Loaded Copper Sulfide Nanoparticles Delivered by Mesenchymal Stem Cells

This study aimed to investigate the regulation of fibroblast phenotypes by MSCs delivering copper sulfide (CuS) nanoparticles (NPs) loaded with CDKN1A plasmids and their role in cartilage repair during osteoarthritis (OA). Single-cell RNA sequencing data from the GEO database were analyzed to identify subpopulations within the OA immune microenvironment. Quality control, filtering, PCA dimensionality reduction, and tSNE clustering were performed to obtain detailed cell subtypes. Pseudotime analysis was used to understand the developmental trajectory of fibroblasts, and GO/KEGG enrichment analyses highlighted biological processes related to fibroblast function. Transcriptomic data and WGCNA identified CDKN1A as a key regulatory gene. A biomimetic CuS@CDKN1A nanosystem was constructed and loaded into MSCs to create MSCs@CuS@CDKN1A. The characterization of this system confirmed its efficient cellular uptake by fibroblasts. In vitro experiments demonstrated that MSCs@CuS@CDKN1A significantly modulated fibroblast phenotypes and improved the structure, proliferation, reduced apoptosis, and enhanced migration of IL-1β-stimulated chondrocytes. In vivo, an OA mouse model was treated with intra-articular injections of MSCs@CuS@CDKN1A. Micro-CT scans revealed a significant reduction in osteophyte formation and improved joint space compared to control groups. Histological analysis, including H&E, Safranin O-Fast Green, and toluidine blue staining, confirmed improved cartilage integrity, while OARSI scoring indicated reduced disease severity. Immunofluorescence showed upregulated CDKN1A expression, decreased MMP13, and reduced α-SMA expression in fibroblast subtypes. Major organs exhibited no signs of toxicity, confirming the biocompatibility and safety of the treatment. These findings suggest that MSCs@CuS@CDKN1A can effectively regulate fibroblast activity and promote cartilage repair, providing a promising therapeutic strategy for OA treatment.

In Situ Classification of Original Rocks by Portable Multi-Directional Laser-Induced Breakdown Spectroscopy Device

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites.

A Joint Prediction of the State of Health and Remaining Useful Life of Lithium-Ion Batteries Based on Gaussian Process Regression and Long Short-Term Memory

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State of Health (SOH) and Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these features are fed into the GPR model for SOH estimation, generating SOH predictions. Finally, the estimated SOH values from the initial cycle to the prediction start point (SP) are input into the LSTM network in order to predict the future SOH trajectory, identify the End of Life (EOL), and infer the RUL. Validation on the Oxford Battery Degradation Dataset demonstrates that this method achieves high accuracy in both SOH estimation and RUL prediction. Furthermore, the proposed approach can directly utilize one or more health features without requiring dimensionality reduction or feature fusion. It also enables RUL prediction at the early stages of a battery’s lifecycle, providing an efficient and reliable solution for battery health management. However, this study is based on data from small-capacity batteries and does not yet encompass applications in large-capacity or high-temperature scenarios. Future work will focus on expanding the data scope and validating the model’s performance in real-world systems, driving its application in practical engineering scenarios.

Comparison of Dimensionality Reduction Techniques to Improve Performance and Efficiency of Logistic Regression in Network Anomaly Detection

Network anomaly detection is a crucial process to identify abnormal network traffic, which may pose a security threat. This research aims to improve the performance and efficiency of Logistic Regression (LR) in network anomaly detection by applying dimension reduction techniques, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (TSVD), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). The performance of each dimension reduction method is evaluated based on accuracy, precision, recall, F1-score, and computation time. The results show that TSVD provides the best performance with 95.86% accuracy, 0.96 precision, 0.96 recall, 0.95 F1-score, and 13.83 seconds computation time. In contrast, ICA showed the worst performance, especially in precision, recall, and F1-score, with values of 0.73, 0.83, and 0.78, respectively. Meanwhile, although t-SNE produces competitive accuracy, it has a high computational cost with an execution time of 1698.54 seconds. These findings show that choosing the right dimension reduction algorithm not only improves detection performance but also supports data processing efficiency, making it highly relevant for large-scale network security scenarios. Keywords: dimensionality reduction, Logistic Regression, network anamoly detection, performance evaluation, Truncated Singular Value Decomposition.

MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis

Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.

Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System

Background: The morphological type of the acromion may play a role in the etiopathogenesis of various pathologies, such as shoulder impingement syndrome and rotator cuff disorders. Therefore, it is important to determine the acromion’s morphological types accurately and quickly. In this study, it was aimed to detect the acromion shape, which is one of the etiological causes of chronic shoulder disorders that may cause a decrease in work capacity and quality of life, on shoulder MR images by developing a new model for image retrieval in Content-Based Image Retrieval (CBIR) systems. Methods: Image retrieval was performed in CBIR systems using Convolutional Neural Network (CNN) architectures and textural-based methods as the basis. Feature maps of the images were extracted to measure image similarities in the developed CBIR system. For feature map extraction, feature extraction was performed with Histogram of Gradient (HOG), Local Binary Pattern (LBP), Darknet53, and Densenet201 architectures, and the Minimum Redundancy Maximum Relevance (mRMR) feature selection method was used for feature selection. The feature maps obtained after the dimensionality reduction process were combined. The Euclidean distance and Peak Signal-to-Noise Ratio (PSNR) were used as similarity measurement methods. Image retrieval was performed using features obtained from CNN architectures and textural-based models to compare the performance of the proposed method. Results: The highest Average Precision (AP) value was reached in the PSNR similarity measurement method with 0.76 in the proposed model. Conclusions: The proposed model is promising for accurately and rapidly determining morphological types of the acromion, thus aiding in the diagnosis and understanding of chronic shoulder disorders.

A mathematical formulation for predicting the deviation of flexible assemblies based on the dimensionality reduction method

A mathematical formulation for predicting the deviation of flexible assemblies based on the dimensionality reduction method

Lightweight Noise Robust Spoofing Attack Detection using Cochleagram and ResNet Amalgamated Features

Abstract Speaker verification, like other biometric technologies, is vulnerable to spoofing attacks. An attacker impersonates a specific target speaker using impersonation, replay, Text-to-Speech (TTS), or Voice conversion (VC) techniques to gain unauthorized access to the system. The work in this paper, proposes a solution that uses an amalgamation of Cochleagram and Residual Network (ResNet) to implement the frontend feature extraction phase of an Audio Spoof Detection (ASD) system. Cochleagram generation, feature extraction-dimensionality reduction and classification are the three main phases of the proposed ASD system. In the first phase, the recorded audios have been converted into Cochleagrams by using Equivalent Rectangular Bandwidth (ERB) based gammatone filters. In the next phase, three variants of Residual Network (ResNet), ResNet50, ResNet41 and ResNet27, one by one, have been used for extracting dynamic features and Resnet50, ResNet41 that are fed to Linear Decrement Analysis (LDA) for dimensionality reduction. At last, in the classification phase, the LDA reduced features have been used for training four different machine learning classifiers such as Random Forest, Naïve Bayes, K-Nearest Neighbour (KNN), and eXtreme Gradient Boosting (XGBoost), individually. The proposed work in this paper concentrates on synthetic, replay, and deepfake attacks. The state-of-the-art ASVspoof 2019 Logical Access (LA), Physical Access (PA), Voice Spoofing Detection Corpus (VSDC) and DEepfake CROss-lingual (DECRO) datasets are utilised for training and testing the proposed ASD system. Additionally, we have assessed the performance of our proposed system under the influence of additive noise. Airplane noise at different SNR rate (0, dB 5dB, 10dB and -5dB) has been added to training and testing audios for the same. From the obtained results, it can be concluded that combination of Cochleagram and ResNet50 with XGBoost classifier outperforms all other implemented systems for detecting fake audios under noisy environment.

Smith’s reduction for random processes and application for stochastic differential equations

Abstract The main focus of this work is a result related to multidimensional stochastic differential equations and one-dimensional SDEs (stochastic differential equations), using the Smith’s π-projection approach. This method is a commonly used technique in mathematics to simplify complex systems by projecting them onto lower-dimensional spaces, which can facilitate analysis and resolution. In the context of multidimensional stochastic processes (i.e., systems involving multiple random variables), extending the Smith’s π-projection approach we mention would likely involve defining a multidimensional version of this projection. This could include extending the concepts of projection and dimension reduction to multiple random variables, as well as analyzing how these projections can simplify multidimensional stochastic differential equations. As an application, we will prove the existence of almost periodic solutions to n-dimensional stochastic differential equations using the Smith projection. In fact, we will generalize the result from the article [M.-A. Boudref and A. Berboucha, Existence of almost periodic solutions of stochastic differential equations with periodic coefficients, Random Oper. Stoch. Equ. 25 2017, 1, 57–70] to dimensions n > 1 {n>1} .

NipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least Squares.

With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension. nipalsMCIA is available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

Exploring the Pharmacogenomic Map of Croatia: PGx Clustering of 522-Patient Cohort Based on UMAP + HDBSCAN Algorithm

Pharmacogenetics is a branch of genomic medicine aiming to personalize drug prescription guidelines based on individual genetic information. This concept might lead to a reduction in adverse drug reactions, which place a heavy burden on individual patients’ health and the economy of the healthcare system. The aim of this study was to present insights gained from the pharmacogenetics-based clustering of over 500 patients from the Croatian population. The data used in this article were obtained by the pharmacogenetic testing of 522 patients from the Croatian population. The patients were clustered based on the genotypes of 28 pharmacologically relevant genes. Dimensionality reduction was employed using the UMAP algorithm, after which clusters were defined using HDBSCAN. Validation of clustering was performed by decision tree analysis and predictive modeling using the RandomForest, XGBoost, and ExtraTrees classification algorithms. The clustering algorithm defined six clusters of patients based on two UMAP components (silhouette score = 0.782). Decision tree analysis demonstrated CYP2D6 and SLCO1B1 genotypes as the main points of cluster determination. Predictive modeling demonstrated an excellent ability to discern the cluster of each patient based on all genes (avg. ROC-AUC = 0.998), CYP2D6 and SLCO1B1 (avg. ROC-AUC = 1.000), and CYP2D6 alone (avg. ROC-AUC = 0.910). Membership in each cluster provided clinically relevant information, in the context of ruling out certain favorable or unfavorable phenotypes. However, this study’s main limitation is its cohort size. Through further research and investigation of a larger number of patients, more accurate and clinically applicable associations between pharmacogenetic genotypes and phenotypes might be discovered.

Assessing Pollution with Heavy Metals and Its Impact on Population Health

Pollution is one of the most important issues currently affecting the global population and environment. Therefore, determining the zones where stringent measures should be taken is necessary. In this study, Principal Component Analysis (PCA), Factor Analysis (FA), and t-distributed Stochastic Neighbor Embedding (t-SNE) were utilized for dimensionality reduction and clustering of data series containing the concentration of 10 heavy metals collected at 14 locations. The Hazard Quotient (HQ) and Hazard Index (HI) were utilized to determine the non-carcinogenic risk to the population in the studied zones. The highest concentrations of metals in the samples were those of Fe, Zn, Mn, and Cr. PCA indicated that Fe and Zn (Co and Cd) had the highest contribution on the first (second) Principal Component (PC). FA showed that the three-factor model is adequate for explaining the variability of pollutant concentrations. The factor loadings revealed the strength of association between variables and factors, e.g., 0.97 for Zn, 0.83 for Cr, and 0.99 for Co. HQ for ingestion, HQing, was the highest for Fe (between 6.10 × 10−5 and 2.57 × 10−4). HQ for inhalation, HQinh, was the biggest for Mn (from 1.41 × 10−3 to 1.95 × 10−3). HI varied in the interval [0.172, 0.573], indicating the absence of a non-carcinogenic risk. However, since values above 0.5 were determined at four sites, continuous monitoring of the pollution in the sampling locations is necessary.

Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks

The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional rule-based methods struggle to cope with the complexity of these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces the AEXB Model, a hybrid deep learning approach that combines the feature extraction capabilities of an AutoEncoder with the classification power of XGBoost. By combining these complementary methods, the model enhances detection accuracy and significantly reduces false positives. The AEXB Model’s methodology encompasses robust preprocessing steps, including data cleaning, scaling, and dimensionality reduction, followed by comprehensive feature engineering and selection techniques, such as Recursive Feature Elimination (RFE) and correlation analysis. By applying this approach to the Intrusion Detection in Smart Home (IDSH) dataset, the model achieves an impressive 97.24% accuracy, demonstrating its effectiveness in identifying anomalous network behavior indicative of MitM attacks. Additionally, the model’s real-time detection capabilities allow for rapid responses to threats, thus providing continuous protection in dynamic smart home environments.

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments can lead to varied outcomes, and how we process those outcomes may depend on our emotional state. Understanding how individuals interpret the sources of uncertainty is crucial for understanding adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility describes how quickly conditions change. Stochasticity, on the other hand, refers to outcome randomness. We investigated how anxiety and apathy influenced people's perceptions of uncertainty, and how uncertainty perception shaped explore-exploit decisions. Participants (N = 1001, non-clinical sample) completed a restless three-armed bandit task that was analyzed using both latent state and process models. Anxious individuals perceived uncertainty as resulting more from volatility, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

Characterizing Sparse Spectral Diversity Within a Homogenous Background: Hydrocarbon Production Infrastructure in Arctic Tundra near Prudhoe Bay, Alaska

We explore a new approach for the parsimonious, generalizable, efficient, and potentially automatable characterization of spectral diversity of sparse targets in spectroscopic imagery. The approach focuses on pixels which are not well modeled by linear subpixel mixing of the Substrate, Vegetation and Dark (S, V, and D) endmember spectra which dominate spectral variance for most of Earth’s land surface. We illustrate the approach using AVIRIS-3 imagery of anthropogenic surfaces (primarily hydrocarbon extraction infrastructure) embedded in a background of Arctic tundra near Prudhoe Bay, Alaska. Computational experiments further explore sensitivity to spatial and spectral resolution. Analysis involves two stages: first, computing the mixture residual of a generalized linear spectral mixture model; and second, nonlinear dimensionality reduction via manifold learning. Anthropogenic targets and lakeshore sediments are successfully isolated from the Arctic tundra background. Dependence on spatial resolution is observed, with substantial degradation of manifold topology as images are blurred from 5 m native ground sampling distance to simulated 30 m ground projected instantaneous field of view of a hypothetical spaceborne sensor. Degrading spectral resolution to mimicking the Sentinel-2A MultiSpectral Imager (MSI) also results in loss of information but is less severe than spatial blurring. These results inform spectroscopic characterization of sparse targets using spectroscopic images of varying spatial and spectral resolution.

Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT images to identify KRAS mutations in NSCLC patients. Both datasets were analyzed using parametric (t test) and non-parametric statistical tests (Mann–Whitney U test) and dimensionality reduction techniques. Afterwards, the proposed ML pipeline was applied to both datasets using a five-fold cross-validation on the training set (70/30 train/test split) before being validated on the other dataset. The results show that the radiomic features are significantly different (Mann–Whitney U test; p < 0.05) between the two datasets, despite the use of identical feature extraction methods. Model transferability is therefore difficult to achieve, which became evident during external testing (F1 score = 0.41). Oversampling, undersampling, clustering and harmonization techniques were applied to balance and harmonize the datasets, but did not improve the classification of KRAS mutation presence. In general, due to only a single moderate result (highest test F1 score = 0.67), the accuracy of KRAS prediction is not sufficient for clinical application. In future work, the complexity of KRAS mutation might be addressed by taking submutations into consideration. Larger multicentric datasets with balanced tumor stages, including multi-scanner datasets, seem to be necessary for building robust predictive models.

Searching for short-timescale radio anomalies using nonlinear dimensionality reduction techniques

Abstract We have searched for anomalous events using 2,520 hours of archival observations from Murriyang, CSIRO’s Parkes radio telescope. These observations were originally undertaken to search for pulsars. We used a machine-learning algorithm based on ResNet and Uniform Manifold Approximation and Projection (UMAP) in order to identify parts of the data stream that potentially contain anomalous signals. Many of these anomalous events are radio frequency interference, which were subsequently filtered using multibeam information. We detected 202 anomalous events and provide their positions and event times. Our results show that the UMAP unsupervised machine learning pipeline effectively identifies anomalous signals in high-time-resolution datasets, highlighting its potential for use in future surveys. However, the pipeline is not applicable for standard searches for dispersed single pulses. We classify the detected events and, in particular, we are currently unable to determine the possible origin of events that last multiple seconds. For these we encourage follow-up observations.

Clinical evaluation of long-read sequencing-based episignature detection in developmental disorders

BackgroundA subset of developmental disorders (DD) is characterized by disease-specific genome-wide methylation changes. These episignatures inform on the underlying pathogenic mechanisms and can be used to assess the pathogenicity of genomic variants as well as confirm clinical diagnoses. Currently, the detection of these episignature requires the use of indirect methylation profiling methodologies. We hypothesized that long-read whole genome sequencing would not only enable the detection of single nucleotide variants and structural variants but also episignatures.MethodsGenome-wide nanopore sequencing was performed in 40 controls and 20 patients with confirmed or suspected episignature-associated DD, representing 13 distinct diseases. Following genomic variant and methylome calling, hierarchical clustering and dimensional reduction were used to determine the compatibility with microarray-based episignatures. Subsequently, we developed a support vector machine (SVM) for the detection of each DD.ResultsNanopore sequencing-based methylome patterns were concordant with microarray-based episignatures. Our SVM-based classifier identified the episignatures in 17/19 patients with a (likely) pathogenic variant and none of the controls. The remaining patients in which no episignature was identified were also classified as controls by a commercial microarray assay. In addition, we identified all underlying pathogenic single nucleotide and structural variants and showed haplotype-aware skewed X-inactivation evaluation directs clinical interpretation.ConclusionThis proof-of-concept study demonstrates nanopore sequencing enables episignature detection. In addition, concurrent haplotyped genomic and epigenomic analyses leverage simultaneous detection of single nucleotide/structural variants, X-inactivation, and imprinting, consolidating a multi-step sequential process into a single diagnostic assay.